IS-LFF is divided into two parts: (i) the agent network parametrised by \(\theta _{a}\) , consisting of an encoder, a self-attention module, and a decision module; (ii) the
leader-follower forest determined by \(w^d\) . Inspired by SchedNet [1]}, we propose a two-stage learning algorithm to train LFF-IS.
